本文關(guān)鍵詞：基于PHD濾波器的多目標(biāo)跟蹤方法研究　出處：《西北工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 多目標(biāo)跟蹤 概率假設(shè)密度濾波器 航跡提取 不均勻雜波 地面機(jī)動(dòng)目標(biāo)

【摘要】：多目標(biāo)跟蹤技術(shù)已經(jīng)廣泛應(yīng)用于軍事和民用領(lǐng)域,但它仍是多學(xué)科、多領(lǐng)域所共同關(guān)心的重點(diǎn)和難點(diǎn)問(wèn)題。近年來(lái),在多目標(biāo)跟蹤問(wèn)題中,隨機(jī)有限集(Random finite set,RFS)方法頗受關(guān)注,而概率假設(shè)密度(Probability Hypothesis Density,PHD)濾波器作為隨機(jī)有限集框架下的多目標(biāo)完全概率密度函數(shù)一階統(tǒng)計(jì)矩近似產(chǎn)物,解決了隨機(jī)有限集的實(shí)際可執(zhí)行度問(wèn)題,且避免了數(shù)據(jù)關(guān)聯(lián)。本文正是基于PHD濾波器,對(duì)多目標(biāo)跟蹤問(wèn)題進(jìn)行了深入研究,主要研究成果如下:1、基于PHD濾波器的全局航跡提取方法研究:針對(duì)PHD濾波器不能提供目標(biāo)連續(xù)航跡信息的問(wèn)題,提出了基于PHD濾波器的全局航跡提取算法。該算法考慮目標(biāo)的全局信息,即考慮相鄰兩個(gè)時(shí)刻的全部目標(biāo)狀態(tài)估計(jì)點(diǎn)的關(guān)聯(lián)性,提出同一時(shí)刻預(yù)測(cè)峰值和估計(jì)峰值之間的一致性度量及一致性置信度的概念,同時(shí)基于專家知識(shí)提出全局航跡提取策略,最后基于一致性置信度及構(gòu)建的航跡提取決策規(guī)則一一提取航跡,實(shí)現(xiàn)PHD的全局航跡提取。仿真結(jié)果表明,該算法可以穩(wěn)定跟蹤目標(biāo),正確起始、維持及終結(jié)航跡,在航跡提取精度上有明顯優(yōu)勢(shì),且計(jì)算量相比是相當(dāng)?shù)摹?、不均勻雜波環(huán)境及低檢測(cè)概率下的改進(jìn)自適應(yīng)PHD濾波器設(shè)計(jì):針對(duì)傳統(tǒng)PHD濾波器在不均勻雜波環(huán)境及低檢測(cè)概率下跟蹤性能急劇下降的問(wèn)題,提出了一種改進(jìn)的自適應(yīng)PHD濾波器,通過(guò)自適應(yīng)確定雜波區(qū),自適應(yīng)選擇量測(cè)及對(duì)權(quán)值較大的高斯項(xiàng)進(jìn)行保護(hù)來(lái)保證算法的快速性和高精度。該濾波器首先利用AP聚類算法對(duì)監(jiān)視區(qū)域內(nèi)滿足一定條件的多幀累積的所有回波進(jìn)行聚類,用凸包確定雜波區(qū),然后再逐觀測(cè)時(shí)刻進(jìn)行PHD預(yù)測(cè)和更新。在PHD預(yù)測(cè)時(shí)不用凸包里的回波,但在PHD更新時(shí),需先自適應(yīng)選擇量測(cè),而后進(jìn)行PHD更新。同時(shí),在該濾波器中,保護(hù)權(quán)值高的高斯項(xiàng),保證其權(quán)值的穩(wěn)定性。仿真結(jié)果表明,該濾波器可以很好的實(shí)現(xiàn)在不均勻雜波環(huán)境下和低檢測(cè)概率情況下的目標(biāo)跟蹤,相比傳統(tǒng)的PHD濾波器,改進(jìn)了目標(biāo)狀態(tài)估計(jì)精度,提高了計(jì)算效率。3、用于地面機(jī)動(dòng)目標(biāo)跟蹤的約束多模型PHD濾波方法研究:考慮地面目標(biāo)運(yùn)動(dòng)受到地形環(huán)境等限制,將地理信息用于地面目標(biāo)跟蹤可有效提高跟蹤精度。在地面目標(biāo)跟蹤中,將地理信息表示成等式約束的形式來(lái)修正目標(biāo)狀態(tài),并采用多模型處理地面目標(biāo)機(jī)動(dòng)時(shí)運(yùn)動(dòng)模式的不確定,提出了一種用于地面機(jī)動(dòng)目標(biāo)的約束多模型PHD濾波器方法。該算法利用模型條件分布和模型的概率,使用多模型方法對(duì)GM-PHD濾波器中的每一個(gè)高斯分量進(jìn)行預(yù)測(cè)和更新,然后將得到的估計(jì)值融合得到對(duì)應(yīng)的目標(biāo)狀態(tài),并將道路信息表示成等式約束形式,將約束加入修正目標(biāo)估計(jì)狀態(tài),完成地面目標(biāo)跟蹤。仿真結(jié)果表明,本文算法可以在雜波環(huán)境下有效的估計(jì)地面機(jī)動(dòng)目標(biāo)的狀態(tài),相比于未用地理信息的MM-GMPHD方法及傳統(tǒng)的GM-PHD濾波器,有效提高了目標(biāo)狀態(tài)估計(jì)精度。
[Abstract]:Multi target tracking technology has been widely used in military and civilian areas, but it is still much discipline, focus and difficult issues of common concern in many fields. In recent years, the problem of multiple target tracking, random finite sets (Random finite set, RFS) method is popular, while the probability hypothesis density (Probability Hypothesis Density. PHD) filter as a multi-objective stochastic finite set under the framework of the full probability density function of the first-order statistical moment approximation product solves the random finite set of actual execution problem, and avoid the data association. This paper is based on the PHD filter, the tracking problem of multiple targets is studied, the main research results are as follows: 1, research on global path extraction method based on PHD filter: provide continuous track information to solve the problem of PHD filter, the global path extraction algorithm is proposed based on PHD filter. This is By considering the global information of the target, which is considered the two adjacent time all target state estimation related point, put forward the same time prediction measure of consistency between the peak and the estimated peak and the same concept of confidence, and put forward the global strategy of expert knowledge extraction based on the track, the track based on consistency and build confidence the decision rules extraction one track, realize the global path extraction of PHD. The simulation results show that the algorithm can correct the tracking stabilization, initiation, maintenance and termination of the track, in the track extraction accuracy has significant advantages, and the amount of calculation is compared to.2, improved adaptive PHD filter design and heterogeneous clutter under low detection probability: Based on the traditional PHD filter tracking performance fell sharply in inhomogeneous clutter environment and low detection probability under the problem, this paper presents an improved adaptive P HD filter, determined by adaptive clutter region, adaptive choice of measurement and protection of the larger weight Gauss to ensure fast and high accuracy algorithm. By clustering all echo multi frame accumulation of the filter using AP clustering algorithm to monitor the area to meet certain conditions, determine the clutter region with a convex hull. Then by observing time PHD prediction and update. In the prediction of PHD without convex hull in echo, but in the PHD update, first choice adaptive measurement, and then update the PHD. At the same time, in the filter, the protection of Gauss high weights, ensure the stability of the weights. The simulation results show that the the filter can achieve good tracking in inhomogeneous clutter environment and low detection probability under the condition of the target, compared with the traditional PHD filter, improved the accuracy of target state estimation, improve the computational efficiency of.3 for ground Research on constraint multi model PHD filtering method for maneuvering target tracking: the ground moving target by considering the terrain environment, geographic information for ground target tracking can effectively improve the tracking accuracy. In ground target tracking, geographic information is expressed as the form of correction constrained target state, uncertain and multi model ground targets maneuvering motion model, a method is presented for ground maneuvering target constrained multiple model PHD filter. The algorithm uses the probability model and conditional distribution model, using the method of multi model prediction and update of each Gauss component in GM-PHD filter, and then the estimated value corresponding to the target state fusion, and road information will be expressed as equality constraints, the constraint is added to the modification of target state estimation, the completion of the ground target tracking. The simulation results show that the The algorithm can effectively estimate the state of ground maneuvering target in clutters, and effectively improve the accuracy of target state estimation compared with the MM-GMPHD method without traditional geographic information and the traditional GM-PHD filter.

【學(xué)位授予單位】：西北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN713

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